| commit | efa694beba9a833a954ee38cf035a990d1c3bea8 | [log] [tgz] |
|---|---|---|
| author | RJ Ascani <rjascani@google.com> | Thu May 23 10:14:16 2024 -0700 |
| committer | GitHub <noreply@github.com> | Thu May 23 17:14:16 2024 +0000 |
| tree | d4ff667159df39126de72a4e54b0cbdebfbf0d21 | |
| parent | d069ba82992f186d99f71e6224685b6752f16dc3 [diff] |
Import visualize from tensorflow, not tflite-micro (#2595) When running the gen_micro_mutable_op_resolver script as a regular python script without bazel, it cannot find visualize because it is not part of the tflite-micro wheel. When running the script with bazel, bazel can pick up visualize from the path tflite_micro/tensorflow/lite/tools as long as tflite-micro wheel is not installed. If it is installed, then bazel will only look at that, which it is not a part of. This PR switches the import to just be from tensorflow, which should work regardless of whether it is from the tf package or relative path. BUG=#2564
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on DSPs, microcontrollers and other devices with limited memory.
Additional Links:
| Build Type | Status |
|---|---|
| CI (Linux) |  |
| Code Sync |  |
This table captures platforms that TFLM has been ported to. Please see New Platform Support for additional documentation.
| Platform | Status |
|---|---|
| Arduino |   |
| Coral Dev Board Micro | TFLM + EdgeTPU Examples for Coral Dev Board Micro |
| Espressif Systems Dev Boards |  |
| Renesas Boards | TFLM Examples for Renesas Boards |
| Silicon Labs Dev Kits | TFLM Examples for Silicon Labs Dev Kits |
| Sparkfun Edge |  |
| Texas Instruments Dev Boards |  |
This is a list of targets that have optimized kernel implementations and/or run the TFLM unit tests using software emulation or instruction set simulators.
| Build Type | Status |
|---|---|
| Cortex-M |  |
| Hexagon |  |
| RISC-V |  |
| Xtensa |  |
| Generate Integration Test |  |
See our contribution documentation.
A Github issue should be the primary method of getting in touch with the TensorFlow Lite Micro (TFLM) team.
The following resources may also be useful:
SIG Micro email group and monthly meetings.
SIG Micro gitter chat room.
For questions that are not specific to TFLM, please consult the broader TensorFlow project, e.g.: